• Miral Patel

Prediction of stock market has been an attractive topic to the stockbrokers. In stock market the decision on when buying or selling stock is important in order to achieve profit. There are number of techniques that can be used to help investors in order to make a decision for financial gain. In this research work I have propose a prediction algorithm that will give the relation between the dependent factor like price and independent factors like opening price, closing price, high value of stock, low value of stock and volume of stocks bought. In this research, I have explained development of stock price prediction with the use of deep learning algorithm. In this work, I am going to use different deep learning architecture for the price prediction of BSE listed company and compares their performance. Here I had used LSTM and RNN algorithms. I had shown comparative study of this two deep learning algorithm. Study shows that RNN gives better performance than LSTM. Accuracy of LSTM is 87% and accuracy of RNN is 89%.

Stock price prediction, Deep Learning, LSTM, RNN

"Stock Price Prediction Using RNN and LSTM ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 11, page no.1069-1079, November-2018, Available :http://www.jetir.org/papers/JETIRK006164.pdf

"Stock Price Prediction Using RNN and LSTM ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 11, page no. pp1069-1079, November-2018, Available at : http://www.jetir.org/papers/JETIRK006164.pdf